code: def compute_and_broadcast(fn):  # pylint: disable=invalid-name
      """
      Compute a value using the function 'fn' only on the specified rank (default is 0).
      The value is then broadcasted to all other ranks.
  
      Args:
      - fn (callable): A function that computes the value. This should not have any side effects.
      - rank (int, optional): The rank that computes the value. Default is 0.
  
      Returns:
      - The computed value (int or float).
      """
      if is_main_process():
          value_scalar = fn()
          value_tensor = torch.tensor(
              value_scalar, device=torch.cuda.current_device()
          ).float()
      else:
          value_tensor = torch.tensor(
              0.0, device=torch.cuda.current_device()
          )  # Placeholder tensor
  
      # Broadcast the tensor to all processes.
      barrier()
      dist.broadcast(value_tensor, src=0)
  
      # Convert the tensor back to its original type (int or float)
      if value_tensor == value_tensor.int():
          return int(value_tensor.item())
      return float(value_tensor.item())
  
file_path: axolotl/src/axolotl/utils/distributed.py
imports: import os
  import pickle
  from contextlib import contextmanager
  import torch
  import torch.distributed
  from accelerate import Accelerator
label_desc: Use image node_id 10 for calling a global function with example usage: compute_and_broadcast(fn) and returns: float, int